Papers with Code 아카이브

Reinforcement Learning (RL)

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 6
평가표: 2
보존된 평가 기록: 3

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/LiangZhang1996/QL_XLight

공식 코드

Leveraging Queue Length and Attention Mechanisms for Enhanced Traffic Signal Control Optimization

https://github.com/Limmen/gym-idsgame

공식 코드

Finding Effective Security Strategies through Reinforcement Learning and Self-Play

https://github.com/Limmen/gym-optimal-intrusion-response

공식 코드

Learning Intrusion Prevention Policies through Optimal Stopping

https://github.com/LinZichuan/AdMRL

공식 코드

Model-based Adversarial Meta-Reinforcement Learning

https://github.com/LinghengMeng/LSTM-TD3

공식 코드

Memory-based Deep Reinforcement Learning for POMDPs

https://github.com/LinxiFan/SECANT

공식 코드

SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies

https://github.com/LittleYUYU/CoaCor

공식 코드

CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning

https://github.com/LittleYUYU/Interactive-Semantic-Parsing

공식 코드

Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement Learning

https://github.com/Liza-Tennant/modeling_moral_choice_dyadic

공식 코드

Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement Learning

https://github.com/LorenJAnderson/uav-2d-greedyshooter-rl

공식 코드

On Solving the 2-Dimensional Greedy Shooter Problem for UAVs

https://github.com/LorenzoFederici/RobustTrajectoryDesignbyRL

공식 코드

Reinforcement Learning for Low-Thrust Trajectory Design of Interplanetary Missions

https://github.com/LostXine/elo-sac

공식 코드

Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?

https://github.com/LucasAlegre/mbcd

공식 코드

Minimum-Delay Adaptation in Non-Stationary Reinforcement Learning via Online High-Confidence Change-Point Detection

https://github.com/LucasAlegre/sumo-rl

공식 코드

A Comparative Study of Algorithms for Intelligent Traffic Signal Control

https://github.com/Lukeeeeee/baconian-project

공식 코드

Baconian: A Unified Open-source Framework for Model-Based Reinforcement Learning

https://github.com/LunjunZhang/world-model-as-a-graph

공식 코드

World Model as a Graph: Learning Latent Landmarks for Planning

https://github.com/MACS-Research-Lab/safe-continual

공식 코드

On the Design of Safe Continual RL Methods for Control of Nonlinear Systems

https://github.com/MAOYIXIU/SCAS

공식 코드

Offline Reinforcement Learning with OOD State Correction and OOD Action Suppression

https://github.com/MAPLLM/MAPICLR2025sub

공식 코드

Improving Planning with Large Language Models: A Modular Agentic Architecture

https://github.com/MH-Hou/EARLY

공식 코드

"Give Me an Example Like This": Episodic Active Reinforcement Learning from Demonstrations

https://github.com/MIRALab-USTC/RL-POMBU

공식 코드

Deep Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy Optimization

https://github.com/MIRALab-USTC/RL-TRACER

공식 코드

Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data Corruptions

https://github.com/MJinXiang/Reasoning-Table

공식 코드

Reasoning-Table: Exploring Reinforcement Learning for Table Reasoning

https://github.com/MLD3/OfflineRL_ModelSelection

공식 코드

Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings

https://github.com/MLD3/RL-Set-Valued-Policy

공식 코드

Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies

https://github.com/MLforHealth/rl_representations

공식 코드

An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare

https://github.com/MadryLab/implementation-matters

공식 코드

Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPO

https://github.com/Mahmoud-Selim/Safe-Reinforcement-Learning-for-Black-Box-Systems-Using-Reachability-Analysis

공식 코드

Safe Reinforcement Learning Using Black-Box Reachability Analysis

https://github.com/MahmudulAlam/Differential-Diagnosis-Using-Transformers

공식 코드

DDxT: Deep Generative Transformer Models for Differential Diagnosis

https://github.com/Mahoumaru/TAdam

공식 코드

TAdam: A Robust Stochastic Gradient Optimizer

https://github.com/MajidMoghadam2006/RL-frenet-trajectory-planning-in-CARLA

공식 코드

An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet Space

https://github.com/MajidMoghadam2006/deepcars-reinforcement-learning

공식 코드

A Hierarchical Architecture for Sequential Decision-Making in Autonomous Driving using Deep Reinforcement Learning

https://github.com/Maluuba/jumping-task

공식 코드

Learning Invariances for Policy Generalization

https://github.com/ManUtdMoon/Safe_MBRL

공식 코드

Safe Model-Based Reinforcement Learning with an Uncertainty-Aware Reachability Certificate

https://github.com/ManipulativeConsultant/AutonomousDrivingCookbook

공식 코드

Adaptive Symmetric Reward Noising for Reinforcement Learning

https://github.com/ManuelFritsche/vpc

공식 코드

Using State Predictions for Value Regularization in Curiosity Driven Deep Reinforcement Learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.